Qualitative analysis of segmentation methods in detection of Atherosclerosis in Diabetic Patients

Today there is an increase in interest for setting up medical system that can screen a large number of people for life threatening diseases, such as Cardio Vascular Diseases (CVD) in Diabetic Patients. In this paper three different methods of segmentation are discussed. K-means and Fuzzy C-means (FC...

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Bibliographic Details
Main Authors: Fahimuddin, S, Rahim, B Abdul, Prasad, M N Giri, SomaSekhar, A, Rao, Jayabhaskar, Ravindraiah, R
Format: Conference Proceeding
Language:English
Subjects:
Atherosclerosis
Biomedical imaging
Cardiology
Clustering algorithms
Diabetes
Fuzzy C-means clustering algorithm
Image processing
Image segmentation
K-means Clustering Algorithm
Pixel
Segmentation
Watershed transformation
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Summary:Today there is an increase in interest for setting up medical system that can screen a large number of people for life threatening diseases, such as Cardio Vascular Diseases (CVD) in Diabetic Patients. In this paper three different methods of segmentation are discussed. K-means and Fuzzy C-means (FCM) are two methods that use distance metric for segmentation. K-means is implemented using standard Euclidean distance metric, which is usually insufficient in forming the clusters. Instead in FCM, weighted distance metric utilizing pixel co-ordinates, RGB pixel color and/or intensity and image texture is commonly used. As the datasets scale increases rapidly it is difficult to use K-means and FCM to deal with massive data. So, the focus of this work is on the Morphological Watershed segmentation algorithm which gives good results on Blood vessel images of Atherosclerosis. The tool used in this work is MATLAB.
DOI:10.1109/INTERACT.2010.5706146